DM Nov-Dec 2023

Dm OPINION: AI
Safety warnings highlight the
importance of AI ethics
While we should undoubtedly proceed with care and caution, underpinning AI
deployment with good data allows organisations to balance regulatory and moral
risks, argues Yohan Lobo, Industry Solutions Manager, Financial Services at M-Files
AI safety and security has been a
hotly discussed topic in recent
times - numerous high-profile
figures expressed concern at the rate of
global AI development at the UK's AI
Safety Summit, held at Bletchley Park.
Even King Charles weighed in on the
subject when virtually addressing the
summit's attendees stating, "There is a
clear imperative to ensure that this
rapidly evolving technology remains
safe and secure." Additionally, in his first
King's speech he set out the UK
government's legislative agenda for the
coming session of parliament. King
Charles explained the government's
intention to establish "new legal
frameworks to support the safe
commercial development" of
revolutionary technologies such as AI.
At M-Files we believe that avoiding the
pitfalls brought to our attention at the
summit and in the King's Speech hinges
on organisations leveraging AI solutions
that are built on a foundation of highquality
data.
Mass adoption of AI presents one of
the most significant opportunities in
corporate history, which businesses will
do their utmost to cash in on, with this
technology capable of delivering
exponential increases in efficiency and
allowing organisations to scale at
speed.
However, concerns rightfully raised at
the UK's Global AI Safety Summit and
reinforced in the King's Speech
demonstrate the importance of
developing AI ethically and ensuring
that organisations looking to take
advantage of AI solutions consider how
they can best protect their customers.
Data quality lies at the heart of the
global AI conundrum - if organisations
intend to start deploying Generative AI
(GenAI) on a wider scale, it's vital that
they understand how Large Language
Models (LLMs) operate and whether
the solution they implement is reliable
and accurate.
The key to this understanding is
having control over the location of the
data the LLM gains its knowledge from.
For example, if a GenAI solution is given
free rein to scour the internet for
information, then the suggestions it
provides will be untrustworthy, as you
can't be sure whether it has come from
a reliable source. Bad data in always
means bad language out.
In contrast, if you only allow a model
to draw from internal company data,
the degree of certainty that any answers
provided can be relied upon is
significantly higher. Any LLMs grounded
in trusted information can be incredibly
powerful tools and a guaranteed way of
boosting the efficiency of an
organisation.
The level of human involvement in AI
integration will also play a crucial role
in its safe use. We must continually
treat AI like an intern, even if a solution
has been operating dependably for an
extended period of time. This means
regular audits and considering the
findings of AI as recommendations
rather than instructions.
Ultimately, companies can contribute
to the safe and responsible development
of AI by only deploying GenAI solutions
that they can trust and that they fully
understand. This begins by controlling
the data the technology is based on and
ensuring that a human is involved at
every stage of deployment.
More info: www.m-files.com
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